Abstract: Current work presents a case study that simultaneously addresses the classical problem of job sequencing and batch sizing in a manufacturing firm. The firm produces engines and transmission sets for automotive industries and is characterized by multi-stage processing of several sub-products followed by the final assembly. The firm processes 11 components using 23 machines to cater customer demand of transmission sets under constraints like machine capacity and delivery schedule. To propose an improvised schedule and batch sizes, a planning model is developed which also aims to improvise specific performance measurement criteria i.e. makespan. The problem is complex due to exceedingly large solution space, which precludes the use of any exact algorithm. A simulation based Genetic Algorithm (GA) approach is thus used to solve this optimization problem. Authors report successful implementation of the approach and demonstrate improvised results over the existing approach of the firm. The work assists operations manager for efficient planning, and constitutes a practical application of simulation-based optimization involving effective monitoring and control of production.